Here I want to apply the projected neighbors graph visualization to the pancreas dataset that is used in the scVelo demo and compare it to the visualization on the U2OS dataset.
Use the reticulate package to use scVelo from within R:
Extract spliced and unspliced data
Extract PCA coordinates
Filter genes
Downsample cells to make things easier
Normalize for dimensional reduction
## Warning in if (!class(counts) %in% c("dgCMatrix", "dgTMatrix")) {: the condition
## has length > 1 and only the first element will be used
## Converting to sparse matrix ...
## Normalizing matrix with 1232 cells and 8724 genes
Dimensional reduction
Run velocyto on panc data
Scores of observed and projected states in PC space
Graph visualization on subset of cells from PC coordinates
Graph visualization on subset of cells from gene expression
using common.genes (intersect of overdispersed genes, odsGenes, and genes in velocity output)
Effects of changing k, distance measure, similarity measure, and similarity threshold:
Using PC generated graph
L1 vs L2 as distance measure:
#using k=10, similarity=cosine, threshold=0.25
set.seed(1)
graphViz(curr.scores.cellsub,proj.scores.cellsub,10,"L1","cosine",0.25,cell.cols.grph,"L1 distance")
graphViz(curr.scores.cellsub,proj.scores.cellsub,10,"L2","cosine",0.25,cell.cols.grph,"L2 distance")
Pearson correlation vs Cosine similarity:
Number of out edges k:
Similarity threshold:
## [1] "Done finding neighbors"
## [1] "Done making edge list"
## [1] "Done making graph"
## delta projections ... sqrt knn ... transition probs ... done
## calculating arrows ... done
## grid estimates ... grid.sd= 0.09961078 min.arrow.size= 0.001992216 max.grid.arrow.length= 0.0610458 done
## [1] "Done finding neighbors"
## [1] "Done making edge list"
## [1] "Done making graph"
## delta projections ... sqrt knn ... transition probs ... done
## calculating arrows ... done
## grid estimates ... grid.sd= 0.09768094 min.arrow.size= 0.001953619 max.grid.arrow.length= 0.0610458 done
## [1] "Done finding neighbors"
## [1] "Done making edge list"
## [1] "Done making graph"
## delta projections ... sqrt knn ... transition probs ... done
## calculating arrows ... done
## grid estimates ... grid.sd= 0.1035558 min.arrow.size= 0.002071116 max.grid.arrow.length= 0.0610458 done
## [1] "Done finding neighbors"
## [1] "Done making edge list"
## [1] "Done making graph"
## delta projections ... sqrt knn ... transition probs ... done
## calculating arrows ... done
## grid estimates ... grid.sd= 0.09911517 min.arrow.size= 0.001982303 max.grid.arrow.length= 0.0610458 done
Cell consistency score: mean correlation b/w cell’s velocity and velocities of nearest neighbors
.. find n nearest neighbors for each cell e.g…
## [1] "Done finding neighbors"
## [1] "Done making edge list"
## [1] "Done making graph"
.. calculate consistency score for each cell..
Number of out edges k:
Similarity threshold: #### Consistency of fdg compared to other embeddings Consistency score in FDG compared to PCA and UMAP computed on same cell subset
## Warning in vattrs[[name]][index] <- value: number of items to replace is not a
## multiple of replacement length
## [1] "Mean consistency scores for PCA, UMAP, FDG"
## [1] 0.4343842
## [1] 0.4314783
## [1] 0.5199737
## [1] "Median consistency scores for PCA, UMAP, FDG"
## [1] 0.4321841
## [1] 0.4309548
## [1] 0.5485402